#https://www.pyimagesearch.com/2016/03/28/measuring-size-of-objects-in-an-image-with-opencv/
from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
import numpy as np
import argparse
import imutils
import cv2


def midpoint(ptA, ptB):
    return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)


ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
                help="path to the input image")
ap.add_argument("-w", "--width", type=float, required=True,
                help="width of the left-most object in the image (in inches)")
args = vars(ap.parse_args())

# grabcut
img = cv2.imread(args["image"])
# mask = np.zeros(img.shape[:2],np.uint8)
# width, height = img.shape[:2]
# bgdModel = np.zeros((1,65),np.float64)
# fgdModel = np.zeros((1,65),np.float64)
#
# # rect = (10,10,width-30,height-30)
# rect = (5, 5, img.shape[:2][1]-5, img.shape[:2][0]-5)
# cv2.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)
#
# mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')
# image = img*mask2[:,:,np.newaxis]
#
# #Get the background
# background = img - image
#
# #Change all pixels in the background that are not black to white
# background[np.where((background > [0,0,0]).all(axis = 2))] =[255,255,255]
#
# #Add the background and the image
# final = background + image

# load the image, convert it to grayscale, and blur it slightly
# image = cv2.imread(args["image"])
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
silverupper = (255, 100, 100)
silverlower = (0, 0, 0)
cv2.imshow("init", hsv)
# gray = cv2.GaussianBlur(gray, (7, 7), 0)
hsv = cv2.GaussianBlur(hsv, (7, 7), 0)
mask = cv2.inRange(hsv, silverlower, silverupper)
# mask = cv2.erode(mask, None, iterations=2)
# mask = cv2.dilate(mask, None, iterations=2)
cv2.imshow("hsv", mask)

# perform edge detection, then perform a dilation + erosion to
# close gaps in between object edges
# thresh = cv2.adaptiveThreshold(hsv, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 19, 9)
edged = cv2.Canny(mask, 50, 100)
edged = cv2.dilate(edged, None, iterations=1)
edged = cv2.erode(edged, None, iterations=1)
cv2.imshow('thresh', edged)
cv2.waitKey(0)

# TODO: threshhold image using harris corner dectector
# find contours in the edge map
# cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
#                         cv2.CHAIN_APPROX_SIMPLE)
# '''draw'''
# # cv2.drawContours(image, cnts, -1, (0,255,0), 3)
# # cv2.imshow('img', image)
#
# cnts = imutils.grab_contours(cnts)
#
# # sort the contours from left-to-right and initialize the
# # 'pixels per metric' calibration variable
# (cnts, _) = contours.sort_contours(cnts)
# pixelsPerMetric = None
#
# # loop over the contours individually
# for c in cnts:
#     # if the contour is not sufficiently large, ignore it
#     if cv2.contourArea(c) < 1000:
#         continue
#
#     # compute the rotated bounding box of the contour
#     orig = img.copy()
#     box = cv2.minAreaRect(c)
#     box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)
#     box = np.array(box, dtype="int")
#
#     # order the points in the contour such that they appear
#     # in top-left, top-right, bottom-right, and bottom-left
#     # order, then draw the outline of the rotated bounding
#     # box
#     box = perspective.order_points(box)
#     cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
#
#     # loop over the original points and draw them
#     for (x, y) in box:
#         cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)
#     # unpack the ordered bounding box, then compute the midpoint
#     # between the top-left and top-right coordinates, followed by
#     # the midpoint between bottom-left and bottom-right coordinates
#     (tl, tr, br, bl) = box
#     (tltrX, tltrY) = midpoint(tl, tr)
#     (blbrX, blbrY) = midpoint(bl, br)
#
#     # compute the midpoint between the top-left and top-right points,
#     # followed by the midpoint between the top-right and bottom-right
#     (tlblX, tlblY) = midpoint(tl, bl)
#     (trbrX, trbrY) = midpoint(tr, br)
#
#     # draw the midpoints on the image
#     cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
#     cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
#     cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
#     cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
#
#     # draw lines between the midpoints
#     cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),
#              (255, 0, 255), 2)
#     cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),
#              (255, 0, 255), 2)
#     # compute the Euclidean distance between the midpoints
#     dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
#     dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
#
#     # if the pixels per metric has not been initialized, then
#     # compute it as the ratio of pixels to supplied metric
#     # (in this case, inches)
#     if pixelsPerMetric is None:
#         pixelsPerMetric = dB / args["width"]
#
#     # compute the size of the object
#     dimA = dA / pixelsPerMetric
#     dimB = dB / pixelsPerMetric
#
#     # draw the object sizes on the image
#     cv2.putText(orig, "{:.1f}cm".format(dimA),
#             (int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,
#                 0.65, (255, 255, 255), 2)
#     cv2.putText(orig, "{:.1f}cm".format(dimB),
#                 (int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
#                 0.65, (255, 255, 255), 2)
#
#     # show the output image
#     cv2.imshow("Image", orig)
#     cv2.waitKey(0)